Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. An event clustering system, comprising: a first processor that is an extraction processor in communication with a managed infrastructure; a second processor that includes one or more of a Non-negative Matrix Factorization, a (“NMF”) processor, a k-means clustering processor and a topology proximity processor, the second processor determining one or more common characteristics or features from events that includes one or more event parameters, the second processor using the common features of events to produce clusters of events relating to the failure or errors in the managed infrastructure, where membership in a cluster indicates a common factor of the events that is a failure or an actionable problem in the physical hardware managed infrastructure directed to supporting the flow and processing of information; a third processor that is a compare and merge processor which receives outputs from the second processor, the compare and merge processor communicating with one or more user interfaces, the second processor or a third processors uses a source address for each event make a change to at least a portion of the managed infrastructure; and wherein each of an event parameter is converted into a numerical representation.
This system relates to event clustering in managed infrastructure environments, addressing the challenge of identifying and grouping related failure or error events to pinpoint actionable problems in physical hardware. The system includes multiple processors working in tandem. A first processor extracts events from the managed infrastructure, capturing event parameters that describe the occurrences. A second processor analyzes these events using techniques such as Non-negative Matrix Factorization (NMF), k-means clustering, or topology proximity analysis to identify common characteristics or features among them. These shared features are used to group events into clusters, where each cluster represents a failure or actionable issue in the infrastructure. A third processor compares and merges the clustered events, refining the groupings and communicating the results through user interfaces. The system also allows for modifications to the infrastructure based on event source addresses. Additionally, event parameters are converted into numerical representations to facilitate analysis. The overall goal is to automate the detection and correlation of infrastructure failures, enabling faster troubleshooting and resolution.
2. The system of claim 1 , wherein an event can include both numeric and non-numerical or text parameters.
A system is designed for processing and analyzing events, where each event can contain both numeric and non-numeric (text) parameters. The system captures and stores event data, allowing for flexible handling of different data types within a single event structure. This capability enables comprehensive event tracking and analysis, accommodating diverse parameter types in a unified framework. The system may include components for event generation, transmission, and processing, ensuring that events with mixed parameter types are correctly interpreted and utilized. By supporting both numeric and text parameters, the system enhances versatility in event-based applications, such as monitoring, logging, or real-time analytics, where different data formats must be integrated seamlessly. The system may also include mechanisms for validating, filtering, or transforming event parameters to ensure consistency and usability across applications. This approach allows for more accurate and detailed event processing, improving decision-making and system performance in environments where events contain varied data types.
3. The system of claim 1 , wherein the system is configured to receive events as they are received.
A system for real-time event processing is disclosed, addressing the need for efficient handling of incoming data streams without delays. The system is designed to process events as they are received, ensuring minimal latency and immediate availability of processed data. This is particularly useful in applications requiring real-time analytics, monitoring, or decision-making, such as financial transactions, IoT sensor data, or network traffic analysis. The system includes a processing module that continuously monitors incoming events and applies predefined rules or algorithms to extract, transform, or analyze the data. It may also include a storage component to retain processed events for historical analysis or compliance purposes. The system is configured to handle high-throughput data streams, ensuring scalability and reliability even under heavy loads. Additionally, the system may integrate with external databases or APIs to enrich event data with additional context or perform cross-referencing. It can also generate alerts or trigger actions based on specific event patterns or thresholds. The architecture is modular, allowing for customization based on specific use cases, such as fraud detection, predictive maintenance, or real-time reporting. The system ensures that events are processed in the order they are received, maintaining data integrity and consistency.
4. The system of claim 2 , wherein the system breaks parameters that are non-numerical.
Technical Summary: This invention relates to a data processing system designed to handle non-numerical parameters in computational models. The system addresses the challenge of integrating non-numerical data, such as categorical or textual information, into analytical processes that typically require numerical inputs. The core functionality involves converting non-numerical parameters into a format compatible with numerical processing, enabling seamless integration into computational workflows. The system includes a preprocessing module that identifies and processes non-numerical data. This module applies techniques such as encoding, normalization, or transformation to convert categorical or textual parameters into numerical representations. For example, categorical variables may be encoded using one-hot encoding or label encoding, while textual data may undergo vectorization or embedding techniques. The system ensures that the converted parameters retain their original meaning while being compatible with numerical analysis. Additionally, the system may include a validation module to verify the accuracy and consistency of the converted parameters, ensuring that the transformation does not introduce errors or biases. The system is designed to be modular, allowing integration with various computational models, including machine learning algorithms, statistical analyses, and optimization processes. By breaking down non-numerical parameters into a processable format, the system enhances the flexibility and applicability of computational models, enabling them to handle diverse data types effectively. This capability is particularly valuable in fields such as data science, artificial intelligence, and automated decision-making, where the ability to process mixed data type
5. The system of claim 1 , wherein an event has one or more fields that are nested values.
Technical Summary: This invention relates to data processing systems that handle structured data, particularly systems that manage events containing nested field values. The core problem addressed is the efficient storage, retrieval, and manipulation of complex data structures where fields may contain nested values, such as arrays, objects, or other hierarchical data types. Traditional systems often struggle with querying or processing such nested structures efficiently, leading to performance bottlenecks or overly complex implementations. The system includes a data processing framework designed to handle events, where each event is a data record containing one or more fields. A key feature is the ability to define fields that are not just simple values but nested values, allowing for hierarchical or multi-level data representation. These nested values can include arrays, objects, or other structured data types, enabling the system to model complex relationships within a single event. The system provides mechanisms to access, query, and manipulate these nested fields while maintaining performance and scalability. This is particularly useful in applications like log analysis, real-time monitoring, or any scenario where events contain rich, structured data with multiple layers of information. The invention ensures that nested field values are properly parsed, stored, and made accessible for downstream processing, improving the flexibility and usability of the data system.
6. The system of claim 5 , wherein nested values are lists and can be nested name-value pairs.
This invention relates to data processing systems that handle nested data structures, particularly those involving hierarchical or recursive arrangements of name-value pairs. The problem addressed is the efficient representation and manipulation of complex data where values can themselves contain further structured data, such as lists or additional name-value pairs. Traditional systems often struggle with such nested structures, leading to inefficiencies in parsing, storage, or retrieval. The system includes a data processing mechanism that supports nested values, where these values are specifically defined as lists or further name-value pairs. This allows for flexible and scalable data organization, accommodating hierarchical relationships without requiring rigid schemas. The nested structure enables efficient representation of complex data models, such as configurations, metadata, or hierarchical datasets, where relationships between elements are dynamic or unknown in advance. The system may also include components for parsing, validating, or transforming such nested data, ensuring consistency and integrity across the hierarchy. By allowing values to be either lists or nested name-value pairs, the system provides a versatile framework for applications requiring deep or recursive data structures, such as document processing, configuration management, or database systems. The invention improves upon prior art by offering a more adaptable and expressive way to handle nested data, reducing the need for flattening or external references.
7. The system of claim 5 , wherein nested values decompose into text or numeric values and the text is converted to one or more numeric values.
This invention relates to a data processing system that handles nested data structures, particularly those containing hierarchical or nested values. The system is designed to address the challenge of extracting and converting nested values into a standardized format for further analysis or processing. Nested values may include text or numeric data, and the system decomposes these values into their constituent parts. When the nested values contain text, the system converts the text into one or more numeric values, enabling consistent data representation and processing. This conversion may involve techniques such as encoding, tokenization, or other numerical representation methods. The system ensures that complex, hierarchical data structures are flattened or transformed into a uniform format, facilitating easier integration with analytical tools or machine learning models. By decomposing and converting nested values, the system enhances data interoperability and enables more efficient data processing workflows. The invention is particularly useful in applications requiring structured data extraction from unstructured or semi-structured sources, such as databases, APIs, or log files.
8. The system of claim 1 , wherein events include alerts.
A system for monitoring and managing events in a computing environment, particularly focusing on alerts, is described. The system collects and processes various events generated by software applications, hardware components, or network devices. These events may include alerts, which are notifications indicating potential issues, errors, or anomalies requiring attention. The system categorizes and prioritizes these events based on predefined criteria, such as severity, source, or type, to facilitate efficient handling. It may also correlate related events to identify patterns or root causes of problems. The system provides mechanisms for filtering, aggregating, and displaying events in a user interface, allowing administrators to monitor system health and respond to critical alerts promptly. Additionally, the system may integrate with other tools or services for automated remediation, logging, or reporting. The goal is to enhance operational efficiency, reduce downtime, and improve the reliability of computing systems by effectively managing and resolving events, especially alerts that signal potential disruptions.
9. The system of claim 1 , wherein parameters include subjects.
A system for managing and analyzing data includes a processing unit configured to receive and process input data, where the input data is structured according to predefined parameters. These parameters define the scope and characteristics of the data being processed. The system further includes a storage unit for storing the processed data and a user interface for interacting with the system. The parameters may include subjects, which represent specific topics, entities, or categories relevant to the data being analyzed. The system is designed to categorize, filter, and retrieve data based on these subjects, enabling efficient organization and retrieval of information. The processing unit applies rules or algorithms to the input data to extract meaningful insights, which are then stored in the storage unit for future reference. The user interface allows users to input data, adjust parameters, and view processed results, facilitating a dynamic and interactive experience. The system may also include additional components such as data validation modules to ensure accuracy and consistency of the processed data. The overall goal is to provide a structured and efficient way to handle data, particularly when dealing with large datasets or complex information structures.
10. The system of claim 1 , wherein parameters include one or more attributes.
A system for managing and analyzing data parameters is disclosed, addressing the challenge of efficiently organizing and processing diverse data attributes in complex systems. The system includes a core framework that collects, stores, and processes data parameters, where each parameter is defined by one or more attributes. These attributes may include metadata, measurement values, or contextual information that describe the parameter's characteristics, behavior, or relationships with other data elements. The system dynamically adjusts parameter attributes based on real-time inputs, ensuring adaptability to changing conditions. Additionally, the system may include modules for parameter validation, normalization, and transformation, ensuring data consistency and compatibility across different applications. The system further supports parameter grouping, prioritization, and filtering to enhance data retrieval and analysis efficiency. By integrating these features, the system provides a robust solution for handling complex data structures in fields such as industrial automation, healthcare monitoring, or environmental sensing, where accurate and adaptable parameter management is critical.
11. The system of claim 1 , wherein an event includes an instance of one or more parameters that are examined or analyzed.
A system is provided for monitoring and analyzing events, where each event represents an instance of one or more parameters that are examined or analyzed. The system collects data from various sources, processes the data to identify events, and evaluates the parameters associated with those events. The parameters may include time, location, status, or other relevant attributes, depending on the application. The system may be used in fields such as industrial monitoring, cybersecurity, or environmental tracking, where detecting and analyzing events is critical for decision-making. By examining the parameters of each event, the system can identify patterns, anomalies, or trends that may require further action. The system may also include features for filtering, categorizing, or prioritizing events based on their parameters, ensuring that the most relevant information is highlighted for users. Additionally, the system may support real-time or historical analysis, allowing users to assess current conditions or review past events for insights. The system's ability to process and analyze event parameters enables more efficient and accurate monitoring, improving operational efficiency and decision-making in various industries.
12. The system of claim 1 , wherein parameters include features.
A system for analyzing and processing data includes a method for extracting and utilizing parameters from input data. The system is designed to handle complex datasets by identifying and isolating specific features within the data. These features are used to derive meaningful parameters that can be further processed or analyzed. The system may include a preprocessing module to prepare the input data for feature extraction, ensuring that the data is in a suitable format for accurate analysis. The extracted features are then used to generate parameters that represent key characteristics of the data. These parameters can be used for various applications, such as data classification, pattern recognition, or predictive modeling. The system may also include a feedback mechanism to refine the feature extraction process based on the results of the parameter analysis, improving accuracy and efficiency over time. The overall goal is to provide a robust and adaptable system capable of handling diverse datasets while accurately identifying and utilizing relevant features to derive useful parameters.
13. The system of claim 1 , wherein a feature is a numerical value of a parameter.
A system for analyzing data involves extracting features from input data to generate a feature vector. The system includes a feature extraction module that processes the input data to identify and quantify relevant characteristics, converting them into numerical values representing parameters. These numerical values are then used to form a feature vector, which serves as a structured representation of the input data for further analysis or machine learning tasks. The system may also include a data preprocessing module to clean or normalize the input data before feature extraction, ensuring consistency and improving the quality of the extracted features. Additionally, a feature selection module may be employed to identify the most relevant features, reducing dimensionality and enhancing computational efficiency. The system is designed to handle various types of input data, such as text, images, or sensor readings, and can be applied in applications like predictive modeling, classification, or anomaly detection. The numerical representation of features allows for standardized processing, enabling machine learning algorithms to interpret and learn from the data effectively.
14. The system of claim 1 , wherein a parameter vector is a concatenation of all of the parameters of an individual event.
The invention relates to a system for processing event data, particularly in applications where individual events are characterized by multiple parameters. The system addresses the challenge of efficiently representing and analyzing complex event data by organizing parameters into a structured format. Specifically, the system concatenates all parameters of an individual event into a single parameter vector. This vector serves as a compact and unified representation of the event, enabling streamlined data processing, storage, and analysis. The concatenation process ensures that all relevant parameters are captured in a sequential manner, preserving their relationships while simplifying subsequent operations. This approach is particularly useful in fields such as event logging, sensor data analysis, or real-time monitoring, where events are often described by multiple interdependent parameters. By consolidating these parameters into a single vector, the system enhances computational efficiency and reduces the complexity of handling high-dimensional event data. The invention may also include additional features, such as normalization or dimensionality reduction techniques, to further optimize the parameter vector for specific applications. The overall goal is to provide a scalable and adaptable framework for managing event-based data in diverse technical domains.
15. The system of claim 14 , wherein a parameter vector is a feature vector.
A system for processing data involves generating a parameter vector that represents a feature vector. The system includes a data processing module configured to receive input data and extract features from the input data. These extracted features are then used to generate a feature vector, which serves as a parameter vector for further analysis or processing. The system may also include a machine learning model that uses the parameter vector to perform tasks such as classification, regression, or clustering. The parameter vector may be normalized or transformed to improve the performance of the machine learning model. The system is designed to handle various types of input data, including but not limited to text, images, or sensor data, and can be applied in applications such as predictive maintenance, medical diagnosis, or fraud detection. The use of a feature vector as a parameter vector allows for efficient and accurate data representation, enhancing the system's ability to make predictions or decisions based on the input data.
16. The system of claim 14 , wherein a manager is an attribute of an event.
A system for managing event attributes in a data processing environment addresses the challenge of efficiently organizing and tracking event-related information. The system includes a data structure that defines events and their associated attributes, where each event can have multiple attributes, including a manager attribute. The manager attribute is a specific property of an event that identifies a responsible entity, such as a person, system, or process, overseeing the event. The system allows for dynamic assignment and modification of the manager attribute, enabling flexible event management. Additionally, the system supports querying and filtering events based on their attributes, including the manager attribute, to facilitate event tracking and analysis. The data structure may also include other event attributes, such as timestamps, status indicators, or metadata, to provide a comprehensive view of each event. The system ensures that the manager attribute is properly linked to the event, maintaining data integrity and enabling efficient event processing. This approach improves event management by centralizing attribute information and providing tools for attribute-based event operations.
17. The system of claim 16 , wherein values of a manager can change from one organization to another.
Technical Summary: This invention relates to a system for managing organizational structures and hierarchical relationships, particularly in dynamic environments where managerial roles and responsibilities may vary across different organizations. The problem addressed is the rigidity of traditional hierarchical systems, which often fail to adapt when managerial roles or authority levels change between different organizational contexts. The system enables flexibility by allowing the values or attributes of a manager to be modified or reassigned as they transition between organizations. This ensures that the manager's role, permissions, or responsibilities are appropriately adjusted to fit the new organizational structure or requirements. The system may include mechanisms for tracking, updating, and enforcing these changes to maintain consistency and accuracy across the organization. This adaptability is particularly useful in large enterprises, multi-organizational networks, or environments where roles frequently shift, such as in mergers, acquisitions, or restructuring scenarios. The system may also integrate with existing organizational databases or management tools to streamline the process of reassigning managerial values.
18. The system of claim 15 wherein parameters are converted into numerical representations with the use of one or more feature vectors.
The invention relates to a system for processing parameters in a technical domain, particularly where parameters need to be converted into numerical representations for analysis or machine learning applications. The problem addressed is the need to efficiently and accurately transform raw or categorical parameters into a structured numerical format that can be used in computational models, such as machine learning algorithms or data processing pipelines. The system includes a feature extraction module that converts parameters into numerical representations using one or more feature vectors. Feature vectors are mathematical constructs that encode the parameters into a multi-dimensional space, allowing for efficient computation and analysis. The system may also include a preprocessing module that standardizes or normalizes the parameters before conversion to ensure consistency and improve model performance. Additionally, the system may incorporate a dimensionality reduction technique to optimize the feature vectors, reducing computational complexity while preserving meaningful information. The numerical representations generated by the system can be used in various applications, such as predictive modeling, classification tasks, or anomaly detection. The system is designed to handle diverse types of parameters, including numerical, categorical, and textual data, making it versatile for different use cases. The use of feature vectors ensures that the converted parameters retain their inherent relationships and patterns, enabling accurate and reliable analysis.
19. The system of claim 18 wherein each parameter is represented by one or more columns of a table in a final feature vector.
A system for organizing and processing data parameters in a structured format involves storing each parameter as one or more columns within a table. This table forms part of a final feature vector, which is a consolidated representation of multiple parameters derived from input data. The system is designed to handle complex datasets by transforming raw input into a structured, tabular format where each parameter is systematically mapped to specific columns. This approach ensures that parameters are easily accessible and can be efficiently processed for further analysis or machine learning tasks. The use of a table structure allows for clear organization and quick retrieval of parameter values, enhancing the system's ability to manage large-scale data efficiently. By representing parameters as columns, the system maintains consistency and scalability, making it suitable for applications requiring high-dimensional data processing. The final feature vector serves as a unified output that encapsulates all relevant parameters, facilitating seamless integration with downstream processes. This method improves data handling by standardizing the representation of parameters, reducing complexity, and enabling more effective data-driven decision-making.
20. The system of claim 18 , wherein the one or more feature vectors are created in order to do a conversion of non-numerical and text parameters.
The invention relates to a system for processing data, particularly for converting non-numerical and text parameters into a structured format suitable for analysis. The system addresses the challenge of handling unstructured or semi-structured data, such as text, categorical variables, or other non-numerical inputs, which are difficult to process in traditional computational models. By converting these parameters into feature vectors, the system enables numerical representation, facilitating machine learning, statistical analysis, or other data-driven applications. The system includes a preprocessing module that extracts and transforms non-numerical and text parameters into a standardized format. This may involve techniques like tokenization, embedding, or encoding to convert qualitative data into numerical feature vectors. The feature vectors are then used in downstream processes, such as classification, clustering, or predictive modeling, where numerical inputs are required. The system may also include validation mechanisms to ensure the accuracy and consistency of the converted features. Additionally, the system may incorporate dimensionality reduction techniques to optimize the feature vectors, reducing computational complexity while preserving meaningful information. This ensures efficient processing and scalability, particularly for large datasets. The overall approach enhances data interoperability, enabling seamless integration with analytical tools and algorithms that rely on numerical inputs. The invention is applicable in fields such as natural language processing, bioinformatics, and automated decision-making systems.
21. The system of claim 18 , wherein one or more feature vectors is created for each parameter of the event.
A system for event analysis generates feature vectors for each parameter of an event to enhance data processing and decision-making. The system captures events from various sources, such as sensors, logs, or user interactions, and extracts relevant parameters associated with each event. For each parameter, the system computes one or more feature vectors, which are numerical representations that encode the parameter's characteristics, relationships, or contextual information. These feature vectors enable advanced analytics, such as pattern recognition, anomaly detection, or predictive modeling, by providing structured, machine-readable data. The system may apply dimensionality reduction techniques, normalization, or encoding methods to optimize the feature vectors for specific applications. By transforming raw event parameters into feature vectors, the system improves the efficiency and accuracy of downstream processes, such as real-time monitoring, automated decision-making, or machine learning model training. The system may also support dynamic feature vector generation, allowing adaptation to changing event characteristics or evolving analytical requirements. This approach enhances the system's ability to process complex, high-dimensional event data while maintaining scalability and performance.
22. The system of claim 21 , wherein one or more feature vectors is created for each parameter of the event and is represented by one or more columns of a table in a final feature vector.
The invention relates to a system for processing event data by generating feature vectors for analysis. The system addresses the challenge of efficiently representing and analyzing complex event data by transforming raw event parameters into structured feature vectors. Each parameter of an event is converted into one or more feature vectors, which are then organized into columns of a table. This structured representation allows for improved data processing, machine learning, and pattern recognition tasks. The system may include components for data ingestion, feature extraction, and storage, where the feature vectors are derived from various event parameters such as timestamps, identifiers, or sensor readings. By organizing these vectors into a tabular format, the system enables scalable and efficient analysis of large-scale event datasets. The invention enhances the ability to detect anomalies, classify events, or predict outcomes based on the structured feature vectors. The system may also support real-time or batch processing, depending on the application requirements. The final feature vector table serves as a unified data structure for downstream analytics, ensuring consistency and compatibility with various analytical tools.
23. The system of claim 18 , where each parameter represents a number where there is a natural ordering or scale or a textual description.
A system for processing and analyzing data parameters where each parameter represents a measurable value with a natural ordering or scale, such as numerical values or textual descriptions. The system includes a data input module that receives data containing multiple parameters, each parameter having a defined ordering or scale. A processing module evaluates the parameters based on their inherent ordering or scale, enabling comparisons, sorting, or ranking. For example, numerical parameters can be compared using mathematical operations, while textual descriptions can be analyzed using predefined categorization or ranking criteria. The system may also include a user interface for displaying processed data, allowing users to interact with the ordered or scaled parameters. Additionally, the system may apply statistical or machine learning techniques to derive insights from the ordered parameters, such as identifying trends, anomalies, or correlations. The system is particularly useful in applications where data parameters have a meaningful sequence or hierarchy, such as financial metrics, performance indicators, or qualitative assessments. By leveraging the natural ordering of parameters, the system enhances data analysis, decision-making, and reporting processes.
24. The system of claim 18 , wherein each parameter represents a type of categorical value or enumeration.
A system for managing and processing data parameters is disclosed, addressing the challenge of efficiently handling diverse data types in computational systems. The system categorizes and processes parameters, where each parameter represents a distinct type of categorical value or enumeration. This allows for structured data organization, enabling precise classification and retrieval of information. The system may include a data processing module that interprets these categorical parameters, ensuring compatibility with various data sources and applications. By standardizing parameter types, the system enhances data consistency and reduces errors in data interpretation. The categorical or enumerated values may include predefined options, such as status codes, user roles, or configuration settings, facilitating streamlined data operations. This approach improves data integrity and simplifies integration with other systems, making it particularly useful in applications requiring structured data handling, such as databases, software configurations, or analytical tools. The system ensures that each parameter is clearly defined, reducing ambiguity and improving system reliability.
25. The system of claim 18 , wherein a parameter is not contained within a single event itself and is derived from an entire set of events.
This invention relates to event data processing systems, specifically for analyzing parameters derived from multiple events rather than individual events. The system collects and processes event data, where certain parameters are not isolated to a single event but are instead calculated or inferred from a broader set of events. For example, a parameter like "average response time" may be determined by aggregating response times across multiple events rather than being directly measured in any single event. The system includes components for event collection, storage, and analysis, with specialized logic to compute derived parameters from event sets. This approach allows for more comprehensive insights into system behavior, performance trends, or user interactions that cannot be captured by examining individual events in isolation. The invention is particularly useful in fields like cybersecurity, performance monitoring, or user behavior analytics, where understanding patterns across multiple events is critical. By deriving parameters from event sets, the system enables more accurate detection of anomalies, performance bottlenecks, or other significant patterns that would otherwise be missed if only single-event data were considered. The system may also include visualization tools to present these derived parameters in a meaningful way, aiding in decision-making and troubleshooting.
26. The system of claim 18 , wherein a column of a table represents a time and can be sorted in any time desired.
A system for organizing and displaying tabular data includes a table structure where at least one column represents a time value. The system allows users to sort the table based on the time column in any desired chronological order, such as ascending or descending, or by specific time intervals. The table may also include additional columns representing other data attributes, which can be sorted independently or in conjunction with the time column. The system may further include a user interface that enables dynamic sorting and filtering of the table based on user input, allowing for efficient data analysis and visualization. The time-based sorting functionality helps users organize and interpret temporal data, improving decision-making processes in applications such as financial reporting, project management, or scientific research. The system may also support additional features like data aggregation, trend analysis, and customizable time formats to enhance usability.
27. The system of claim 26 , wherein an importance of a parameter is based on time.
A system for dynamically adjusting the importance of parameters in a technical process or analysis, where the significance of each parameter varies over time. The system monitors and evaluates multiple parameters, assigning different weights or priorities to them based on temporal factors such as time of day, seasonal changes, or real-time conditions. This dynamic weighting allows the system to adapt to changing circumstances, improving accuracy or efficiency in applications like predictive modeling, control systems, or decision-making processes. The system may include a data collection module to gather parameter values, an analysis module to assess their relevance, and an adjustment module to modify their importance over time. By continuously updating parameter importance, the system ensures that the most relevant factors are prioritized at any given moment, enhancing performance in time-sensitive applications. The system may also integrate historical data to refine its temporal weighting logic, ensuring long-term adaptability. This approach is particularly useful in fields where parameter relevance fluctuates, such as environmental monitoring, financial forecasting, or industrial automation.
28. The system of claim 27 , wherein time is represented in at least one of: an absolute value; an assembled parameter, in order of arrival; ordered by time; and order by severity.
This invention relates to a system for managing and processing time-based data, particularly in environments where temporal information must be organized and analyzed for decision-making or operational efficiency. The system addresses the challenge of handling time data in various formats and ensuring it is accurately represented and utilized for tasks such as scheduling, event logging, or prioritization. The system includes a data processing module that receives time-related inputs, which may include timestamps, event logs, or other temporal data. These inputs are processed to represent time in multiple ways: as an absolute value (e.g., a specific date and time), an assembled parameter based on arrival order (e.g., sequential processing of events as they occur), ordered by time (e.g., chronological sorting), or ordered by severity (e.g., prioritizing events based on urgency or impact). This flexibility allows the system to adapt to different use cases, such as real-time monitoring, historical analysis, or critical event management. The system may also include a user interface or output module to display or transmit the processed time data in a structured format, enabling users or other systems to interpret and act on the information. The ability to represent time in multiple formats ensures compatibility with various applications, from log analysis to emergency response systems. The invention improves efficiency by standardizing time data handling and reducing errors in time-based decision-making.
29. The system of claim 1 , wherein parameters can have multiple attributes.
A system is disclosed for managing parameters in a technical or computational environment, where each parameter can be associated with multiple attributes. This system addresses the challenge of handling complex data structures where parameters require flexible and dynamic attribute assignments. The system allows for the definition and storage of parameters, each of which can have one or more attributes, enabling detailed customization and organization of data. These attributes may include metadata, constraints, or other descriptive properties that enhance the parameter's functionality within the system. The system further supports operations such as parameter retrieval, modification, and validation based on these attributes, ensuring consistency and accuracy in data processing. By allowing multiple attributes per parameter, the system provides a scalable and adaptable framework for managing diverse and evolving data requirements in applications such as software development, data analysis, or industrial automation. The system may integrate with existing databases or computational frameworks to facilitate seamless parameter management across different platforms. This approach improves efficiency, reduces errors, and supports complex workflows where parameters must be dynamically configured or adjusted.
30. The system of claim 19 , wherein the system provides for different categories as well as the creation of as many columns as there are categories.
A system is designed to organize and display data in a structured format, addressing the challenge of efficiently categorizing and presenting large datasets. The system allows users to define multiple categories, each representing a distinct classification or grouping of data. For each category, the system automatically generates a corresponding column, ensuring that data is visually separated and easily identifiable. This approach enhances data organization by providing a clear, columnar structure that aligns with the defined categories. The system dynamically adjusts the number of columns based on the number of categories, ensuring scalability and adaptability to varying data structures. This feature is particularly useful in applications requiring flexible data presentation, such as project management, inventory tracking, or analytical dashboards. By automating the creation of columns, the system reduces manual effort and minimizes errors associated with manual data organization. The system may also include additional functionalities, such as filtering, sorting, or customizing the appearance of columns, to further improve usability and data accessibility. The overall design aims to streamline data management processes while maintaining clarity and efficiency in data representation.
31. The system of claim 13 , wherein severity is a number of a feature vector and occupies one column of the feature vector.
A system for analyzing data using feature vectors includes a method for determining the severity of an event or condition. The system processes input data to generate a feature vector, where each feature represents a distinct characteristic of the data. One of these features is designated as the severity metric, which quantifies the intensity or criticality of the event. This severity value is stored as a single numerical value within the feature vector, occupying one dedicated column. The system may further include preprocessing steps to normalize or scale the input data before feature extraction, ensuring consistency in the severity measurement. The feature vector, including the severity value, is then used for further analysis, such as classification, clustering, or anomaly detection. The system may also apply machine learning models to predict or assess the severity based on historical or real-time data. The severity feature can be used independently or in combination with other features to derive insights or trigger automated responses. The system is designed to handle large-scale data processing, enabling real-time or batch analysis of severity levels across multiple data sources.
32. The system of claim 19 , wherein a text based parameter extraction method is used for a word, wherein the text based parameter can include shingles and tokenization, and are used that for multiple letter segments of a word.
This invention relates to a system for extracting text-based parameters from words to improve natural language processing or text analysis. The system addresses the challenge of accurately identifying and processing segments of words, such as shingles (overlapping or non-overlapping sequences of characters) and tokens (individual words or subwords), to enhance tasks like text classification, search, or machine learning. The method involves breaking down words into multiple-letter segments, which can then be analyzed for patterns, similarities, or other linguistic features. This approach helps in tasks where word-level analysis is insufficient, such as handling misspellings, slang, or domain-specific terminology. The system may integrate with larger text processing pipelines, where extracted parameters are used for further analysis, such as feature extraction, clustering, or semantic modeling. The use of shingles and tokenization allows for flexible and granular text representation, improving accuracy in applications like document retrieval, sentiment analysis, or language modeling. The invention focuses on optimizing the extraction process to handle variations in word structure efficiently.
33. The system of claim 32 , wherein the system is configured to receive a number of shingles.
Technical Summary: The invention relates to a data processing system designed to handle and analyze text data, specifically focusing on the extraction and processing of shingles. Shingles are overlapping sequences of words or tokens extracted from text, commonly used in natural language processing (NLP) and information retrieval tasks. The system is configured to receive a specified number of shingles, which are generated by breaking down input text into fixed-length segments. These shingles are then used for tasks such as document similarity comparison, duplicate detection, or feature extraction in machine learning models. The system includes components for generating shingles from input text, where the shingles are of a predefined length and may overlap to ensure comprehensive coverage of the text. The system can also process these shingles to identify patterns, compute similarity metrics, or index them for efficient retrieval. The ability to receive and process a variable number of shingles allows the system to adapt to different text lengths and analysis requirements, improving flexibility in applications like plagiarism detection, text clustering, or search engines. The invention addresses the challenge of efficiently representing and comparing text data by leveraging shingles, which provide a structured way to capture local and global text features. This approach enhances accuracy in tasks requiring text similarity assessment or content analysis, making it suitable for applications in digital libraries, content management systems, and AI-driven text processing pipelines.
34. The system of claim 33 , wherein a parameter can include a plurality of words and a plurality of host names.
This invention relates to a system for managing and processing parameters in a networked environment, particularly for handling configurations or settings that involve multiple words and host names. The system is designed to address challenges in parameter management where configurations may include complex or multi-component identifiers, such as those used in network routing, security policies, or application settings. The system allows parameters to be defined with a plurality of words and host names, enabling flexible and dynamic configuration handling. This capability is useful in scenarios where parameters must reference multiple systems or services, such as in distributed computing, cloud environments, or multi-tenant architectures. The system ensures that parameters can be accurately parsed, validated, and applied across different networked components, improving system interoperability and reducing configuration errors. The invention builds on a base system that processes parameters, adding the ability to handle composite parameters that include both textual and host-based identifiers. This enhances the system's adaptability to real-world use cases where configurations often involve multiple interconnected elements. The system may be used in network management, software deployment, or security policy enforcement, where precise parameter handling is critical.
35. The system of claim 34 , wherein a number of columns in the table required is the number of unique shingles across all host names.
The system relates to data processing and storage, specifically for managing and analyzing host names in a network. The problem addressed is the efficient storage and retrieval of host name data, particularly when dealing with large-scale network monitoring or security applications. Host names often contain repetitive or similar patterns, and traditional storage methods may waste space or require excessive processing to identify unique elements. The system includes a table that stores shingles, which are small, overlapping substrings extracted from host names. These shingles are used to represent the host names in a compact and efficient manner. The table is structured to minimize redundancy by storing only unique shingles across all host names, reducing storage requirements and improving processing speed. The number of columns in the table is determined by the number of unique shingles present in the entire dataset of host names. This ensures that the table dynamically adapts to the diversity of host names being processed, optimizing both storage and computational efficiency. The system may also include mechanisms for generating shingles from host names, indexing the shingles for quick lookup, and reconstructing host names from the stored shingles when needed. This approach is particularly useful in applications such as network traffic analysis, intrusion detection, or domain name system (DNS) monitoring, where efficient handling of large volumes of host name data is critical. By focusing on unique shingles, the system avoids redundant storage and speeds up operations like pattern matching or anomaly detection.
36. The system of claim 23 , wherein the system is configured to look at individual phrases rather than shingles.
Technical Summary: This invention relates to natural language processing (NLP) systems designed to analyze text data. The problem addressed is the inefficiency of traditional text analysis methods that rely on shingles (overlapping sequences of words) for tasks like document similarity, classification, or information retrieval. Shingles can be computationally expensive and may not capture semantic meaning effectively. The system improves upon prior approaches by analyzing individual phrases instead of shingles. Phrases are contiguous sequences of words that form meaningful units, such as noun phrases, verb phrases, or technical terms. By focusing on phrases, the system can better preserve semantic relationships and reduce noise from irrelevant word combinations. The system is configured to extract, normalize, and compare phrases across documents or text segments, enabling more accurate and efficient text analysis. The system may include components for phrase extraction, such as part-of-speech tagging and dependency parsing, to identify meaningful phrases. It may also include phrase normalization techniques to handle variations in phrasing (e.g., "machine learning" vs. "ML"). The comparison module evaluates phrase similarity using techniques like semantic embeddings or string similarity metrics. This approach enhances performance in applications like document clustering, plagiarism detection, and search engines. The invention improves over prior art by reducing computational overhead and improving the relevance of text analysis results.
37. The system of claim 36 , wherein the feature vector has as many columns in the table as there are unique words.
The system relates to natural language processing and data analysis, specifically for organizing and analyzing text data using feature vectors. The problem addressed is the efficient representation and processing of text data in a structured format, particularly for tasks like machine learning, information retrieval, or text classification. The system generates a feature vector from text data, where the feature vector is structured as a table with rows representing individual text samples and columns representing unique words. Each cell in the table contains a value indicating the presence or frequency of a word in a given text sample. This allows for quantitative analysis of text data, enabling algorithms to process and compare text samples based on their word content. The system may further include preprocessing steps to normalize or transform the text data before generating the feature vector, ensuring consistency and improving the accuracy of subsequent analysis. The feature vector can be used in various applications, such as document classification, sentiment analysis, or topic modeling, by leveraging the structured representation of text data. The system ensures that the feature vector accurately reflects the vocabulary of the text samples, with each column corresponding to a unique word, facilitating efficient computation and interpretation of text-based data.
38. The system of claim 37 , wherein manager changes from organization to organization, where manager is a term provided by an organization.
A system for managing organizational structures involves dynamically assigning managers to different organizations within a larger system. The system allows a manager, defined as a term provided by an organization, to transition between organizations. This enables flexible reallocation of managerial roles across multiple organizational units. The system may include a database storing organizational hierarchies, user roles, and manager assignments, along with a processing module to update manager assignments when organizational changes occur. The system ensures that managerial responsibilities are properly tracked and updated as managers move between organizations, maintaining accurate organizational records. This approach supports dynamic workforce management, allowing organizations to adapt to changing structures without manual intervention. The system may also include validation checks to ensure that manager transitions comply with organizational policies. The overall solution improves efficiency in managing hierarchical relationships within complex organizational frameworks.
39. The system of claim 38 , wherein the feature vector columns of the table represent description and manager.
Technical Summary: This invention relates to a data processing system for organizing and managing feature vectors in a tabular format. The system addresses the challenge of efficiently storing and retrieving structured data, particularly in applications involving machine learning or data analysis, where feature vectors are commonly used to represent complex data points. The system includes a table structure where each row corresponds to a distinct data entry, and each column represents a specific feature or attribute of that entry. In this context, the feature vector columns of the table are specifically designated to represent two distinct types of information: "description" and "manager." The "description" column stores textual or categorical data that describes the characteristics or properties of the data entry, while the "manager" column stores information related to the entity or process responsible for overseeing or managing that entry. This dual-column structure allows for efficient querying and filtering of data based on either descriptive attributes or managerial oversight. The system may also include additional components, such as data processing modules for transforming raw data into feature vectors, indexing mechanisms for optimizing search operations, and user interfaces for visualizing or interacting with the tabular data. The overall design ensures that feature vectors are stored in a structured, queryable format, enabling applications such as data analysis, machine learning model training, or decision support systems to access and manipulate the data efficiently.
40. The system of claim 26 , wherein the system is configured to examine through tokenization to convert sentences into words and can get rid of stop words.
This invention relates to natural language processing (NLP) systems designed to analyze and process textual data. The system is configured to perform tokenization, which involves breaking down sentences into individual words or tokens. Additionally, the system filters out stop words—common words like "the," "and," or "is"—that do not contribute significant meaning to the text. By removing these irrelevant words, the system enhances the efficiency and accuracy of subsequent NLP tasks, such as text classification, sentiment analysis, or information retrieval. The system may also include preprocessing steps to clean and normalize the text before tokenization, ensuring consistent input for analysis. This approach improves computational efficiency by reducing the dataset size and focusing on meaningful content. The invention is particularly useful in applications requiring automated text analysis, such as search engines, chatbots, or document summarization tools. By automating the removal of stop words, the system streamlines text processing workflows and enables faster, more accurate insights from large volumes of text data.
41. The system of claim 32 , wherein stemming is used to de-pluralize words.
This invention relates to a system for processing natural language text, specifically addressing the challenge of improving text analysis by normalizing word forms. The system includes a text processing module that receives input text and applies stemming techniques to reduce words to their base or root forms. Stemming is used to de-pluralize words, converting plural forms (e.g., "cats") to their singular equivalents (e.g., "cat"). This normalization step enhances text analysis by reducing variations in word forms, improving accuracy in tasks such as information retrieval, document clustering, and semantic analysis. The system may also include additional preprocessing steps, such as tokenization and stopword removal, to further refine the input text before stemming is applied. The output of the system is a normalized text representation that facilitates more consistent and efficient text processing in downstream applications. The invention is particularly useful in applications requiring high-precision text analysis, such as search engines, machine learning models, and natural language processing pipelines.
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September 3, 2019
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